Left, Right or Behind: Steering Position in AI Images

AI image models often place objects in the wrong position relative to each other because they read your prompt as a loose collection of concepts rather than a floor plan: the model knows a cat and a couch belong in the frame, but not that the cat should sit to the left of the couch. On the T2I-CompBench benchmark, common open models like Stable Diffusion score around 0.15 on a 0-to-1 scale for spatial relations, one of their weakest categories. Research (Getting it Right, ECCV 2024) points to a key cause: explicit position words like "to the left of" barely appear in the captions models train on. Below you'll read where it goes wrong and how to steer position anyway.

Why does AI mix up left, right and behind?

An image model keeps no floor plan while it generates. It recognizes the subjects you name, but doesn't closely track where they sit relative to each other. So "a lamp to the right of the chair" lands just as easily on the left, or both objects end up in the middle.

Two tendencies make it worse. Models lean toward placing the first-named object prominently, and they often give a different result for relations that are really the same ("A left of B" versus "B right of A"). Position, to the model, isn't a hard rule but a loose hint.

The core of it: the model knows there should be space between your objects, but it keeps no exact floor plan while it draws.

Which positions go wrong most often?

Left and right get swapped most literally, because they're mirror images: to the model the difference is small, and it regularly picks the wrong side. Above and below are a bit more stable, but "on top of" or "stacked" stays tricky the moment objects have to touch.

Depth is hardest. "Behind", "in front of" and "in the background" ask the model to imply distance in a flat image, and that's exactly where it slips: the object that should sit behind ends up beside or in front.

And the more relations you cram into one sentence, the faster it collapses. "The dog to the left of the couch, the lamp behind it and the cat on top" is three floor plans at once. Ask for one clear relation and your odds of a correct image are highest.

How to anchor position in your prompt

The best control is a combination of one clear relation, a fixed anchor point, and a concrete layout. You don't force the model to calculate, but you make each object's spot specific enough that it's harder to ignore.

1. Choose one relation per image. Describe a single spatial relation at a time. Need more objects placed neatly? Build the image in steps or add the rest later.
2. Name the anchor first. Put the object that stays fixed up front and hang the rest off it: "a wooden table, with a green chair to its left". The model leans on what it reads first.
3. Describe the layout concretely. "On the left of the frame", "bottom right", "in the foreground" works better than a bare "next to". An explicit spot gives the model something to hold on to.
4. Make the objects different. Two identical items easily merge or swap. Give them their own color, size, or material and they stay apart.
5. Test small. Generate one image first, check the placement, and adjust your prompt before making a whole series. If you want to build subject, position, and scene neatly separated, the prompt generator helps you hold that structure.

Fix a wrong placement without redoing everything

If one object is just slightly off, you usually don't need to regenerate from scratch. A targeted fix is faster and cheaper.

• Move or remove with inpainting. In the photo editor, select the misplaced object and let the AI fill the area, or put the element back on the correct side in the same style.
• Use a reference. With image-to-image in the photo generator, you pass an existing image or a rough sketch as the base, so the composition stays closer to what you meant.
• Regenerate with a different seed. Sometimes one new render with a small change in order or wording is enough to get the position right.

Because you pay per render and per edit, you only pay for the correction you actually need, instead of redoing the whole image ten times.

Frequently asked questions

Why does AI put my objects in the wrong place?

Because an image model keeps no exact floor plan. It recognizes your subjects but barely tracks their relative position. Spatial relations are also among the weakest categories in benchmarks, partly because position words rarely appear in the training captions.

Is left/right harder than above/below?

Left and right get swapped most literally, because they're each other's mirror image. Above and below are a bit more stable. The hardest are depth relations like "behind" and "in front of", which ask for distance in a flat image.

Does repeating the position in my prompt help?

Barely. Naming the relation again rarely fixes it. What does help: one clear relation per image, naming the anchor object first, and describing the spot concretely ("on the left of the frame").

How do I get two objects placed exactly right?

Generate a base image with one clear relation, then correct the placement with inpainting: move or remove the object that sits wrong. That way you keep control without re-rendering the whole image.

Spatial relations stay a weak spot of AI image models, but with one clear relation, a fixed anchor, and a targeted fix you can keep your objects' positions well under control. Create an account and try it on your next image.